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DaedalMap UN Sustainable Development Goals

by com.daedalmap

Query Dataset

query_dataset
Read-only

Generic structured query for direct source_id or pack_id access using the same contract as POST /api/v1/query/dataset. Free packs: currency, distributed_manufacturing, floods, nri, owid, un_sdg, un_wpp, volcanoes, world_bank_wdi. Paid packs: earthquakes, hurricanes, tornadoes, tsunamis, wildfires, world_factbook, worldpop (x402 Base USDC).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoOptional sort instructions for row-returning queries.
limitNoMaximum number of rows to return for the requested source or pack.
outputNoOptional output controls such as response format hints.
filtersNoStructured filters including time, region_ids, and compare clauses.
metricsNoMetric ids to return. Use event_count for aggregate counts when supported.
pack_idNoPack identifier from get_catalog. Newly catalog-admitted packs require no MCP schema change.
source_idNoConcrete source id such as 'earthquakes_events', 'volcanoes_events', 'hurricanes_events', or 'un_sdg/01'.
request_idNoOptional caller-supplied request id for tracing and idempotency.

TDQS

A3.8/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The readOnlyHint annotation already declares this as a read-only operation, so the description's burden is lower. It adds a reference to the API contract but does not disclose potential side effects, limitations, or response behavior beyond what annotations already cover. This is adequate but not exceptional.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, dense sentence that front-loads the main purpose and then lists relevant packs. It is efficient with no fluff, though the enumeration of pack names adds length. Overall it is well-structured and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 8 parameters including nested objects and no output schema, the description does not explain the response format or how to structure complex queries. It references the API contract as a hint, but that is indirect. The schema covers parameters, yet the overall contextual completeness for an agent would benefit from a brief note on expected return behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all 8 parameters are already well-described in the input schema. The tool description adds little beyond the schema, offering only examples of source IDs and pack names. Since the schema carries the explanatory weight, the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it is a generic structured query for direct source_id or pack_id access, referencing the same contract as POST /api/v1/query/dataset. This distinguishes it from siblings like get_catalog (metadata) and get_pack (pack info) because it is the data-retrieval tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context that this tool is for querying data directly by source or pack ID, and lists the available free and paid packs. However, it does not explicitly exclude alternatives or state when to use get_catalog/get_pack instead, so it falls short of full 'when-not-to-use' guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: get_catalog discovers available packs, get_pack provides metadata for a specific pack, get_tool_help gives usage guidance for a tool, and query_dataset executes queries. There is no overlap or ambiguity in their roles.

Naming Consistency3/5

Three tools follow a 'get_*' pattern (get_catalog, get_pack, get_tool_help), but query_dataset breaks the pattern with a different verb. The objects vary (catalog, pack, tool_help, dataset) without a strict verb_noun structure, but the names remain understandable and readable.

Tool Count5/5

With 4 tools, the server is well-scoped for a data access/query facade. Each tool covers a necessary step in the workflow (discover, inspect, get help, query) without bloat, fitting comfortably in the ideal 3-15 range.

Completeness4/5

The tool surface covers the full user journey for a data query server: discovery (get_catalog), metadata inspection (get_pack), tool guidance (get_tool_help), and actual querying (query_dataset). Minor gaps exist, such as no explicit way to list all available tools without using tools/list, but that is an MCP protocol concern rather than a domain coverage gap.

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